Technical Field
The present disclosure relates generally to systems and methods for dynamically generating and updating patrol schedules based on historic demand event data and other information.
Background
The United States has more than 18,000 state and local law enforcement agencies. Current budget constraints and recruitment difficulties make it ever more important for police chiefs and command staff within these agencies to leverage existing resources to achieve policing objectives. Patrol assignments must be made astutely to ensure that response-time targets are met for new calls for police assistance, timely backup is provided to police officers responding to calls when the number of officers needed for the call increases (calls that “escalate”), neighborhood policing is achieved, crime is proactively deterred, and the probability of apprehending criminals is maximized. Currently, however, police chiefs have no readily available tool to dynamically forecast demand for service from historical databases of police incident reports (i.e., reports on requests for police assistance), and then to assign patrol officers to optimally service that anticipated need.
Law enforcement agencies serve a critical role in society by providing a number of important first-responder operations ranging from health and welfare checks to priority-one response to incidents where danger to the public is imminent. As demand for police services continues to increase and agencies struggle to fill their ranks with qualified personnel, law enforcement agencies are facing a growing challenge to achieve departmental goals and meet taxpayers' expectations in the context of flat-line budgets, or even budget cuts, resulting in layoffs, hiring freezes, training cutbacks, and various other operational losses. The result is an overabundance of agencies that are highly resource-constrained and being told to “do more with less.”
To address manpower and budget issues, some agencies have filled positions with citizen-volunteer programs, but not all jobs in public safety may be filled in such a manner. What is needed is a means for analyzing vast amounts of data very quickly and prescribing optimal patrol plans to maximize the utility of available resources.
Beyond the problem of constrained resources, agencies find it incredibly difficult, if not impossible, to extract timely, actionable information from large databases of historic incidents to accurately forecast demand in real-time. Without timely, dynamic information, patrol plans can be significantly inefficient, with officers driving on main thoroughfares in uncoordinated patterns through their cities or sectors with limited insight regarding how to re-plan their routes in response to service calls. With the severity of the resource constraints described above, tools are needed to help agencies achieve their objectives, such as minimizing response times to new calls and escalating calls, ensuring sufficient region revisit rates, and allowing time for community policing.
The disclosed methods and apparatus are directed to overcoming one or more of the problems set forth above and/or other problems or shortcomings in the prior art.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.